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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Improving the measurement robustness of active stereo 3D sensors by optimization of shifted aperiodic fringe patterns
 Harding, K.G. ; Society of PhotoOptical Instrumentation Engineers SPIE, Bellingham/Wash.: Dimensional Optical Metrology and Inspection for Practical Applications IX : 27 April  8 May 2020, Online Only, United States Bellingham, WA: SPIE, 2020 (Proceedings of SPIE 11397) ISBN: 9781510635715 ISBN: 9781510635722 Paper 1139702, 12 S. 
 Conference "Dimensional Optical Metrology and Inspection for Practical Applications" <9, 2020> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IOF () 
Abstract
Pattern projectionbased 3D sensors are widely used for contactless, nondestructive optical 3D shape measurements. In previous works, we have shown 3D measurement systems based on stereo matching between two cameras with GOBOprojected aperiodic fringe patterns. In this contribution, we demonstrate a method to optimize the projection patterns for high measurement robustness, i.e., high completeness of the resulting point cloud with low probability of outliers. To calculate the 3D coordinates of an object point by triangulation, a pixel correspondence between the two cameras must be found. The search for such pixel correspondences can be broken into two parts: a coarse correspondence search and a subpixelaccurate refinement. The former is responsible for the completeness and correctness of the 3D result, while the quality of the latter determines the accuracy. The correctness of the correspondence search depends on the property of the projection pattern to uniquely encode each point on the measurement object. If the pattern is very selfsimilar, the points are not well distinguishable from each other and there is a high probability of mismatches during correspondence search. We introduce a mathematical measure to evaluate the selfsimilarity of a GOBOprojected fringe pattern. This measure operates on patterns, which we simulate with a simplified 1D model. Based on this measure and its derivatives, we developed an algorithm to optimize the fringe patterns. We compare results achieved with unoptimized and optimized fringe patterns.